



import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  Tuple			     =			logging.get_logger(__name__)

a      :  List[str]			     =			{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''encodec'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 1_2.0, 2_4.0],   SCREAMING_SNAKE_CASE_=24000,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=128,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=[8, 5, 4, 2],   SCREAMING_SNAKE_CASE_="weight_norm",   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_="reflect",   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=1.0,   SCREAMING_SNAKE_CASE_=1024,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=True,   **SCREAMING_SNAKE_CASE_,   )       ->   Dict:
						UpperCAmelCase_:		Any							      =     target_bandwidths
						UpperCAmelCase_:		Any							      =     sampling_rate
						UpperCAmelCase_:		List[str]							      =     audio_channels
						UpperCAmelCase_:		Union[str, Any]							      =     normalize
						UpperCAmelCase_:		int							      =     chunk_length_s
						UpperCAmelCase_:		Tuple							      =     overlap
						UpperCAmelCase_:		Tuple							      =     hidden_size
						UpperCAmelCase_:		Tuple							      =     num_filters
						UpperCAmelCase_:		Dict							      =     num_residual_layers
						UpperCAmelCase_:		Optional[Any]							      =     upsampling_ratios
						UpperCAmelCase_:		Union[str, Any]							      =     norm_type
						UpperCAmelCase_:		int							      =     kernel_size
						UpperCAmelCase_:		Tuple							      =     last_kernel_size
						UpperCAmelCase_:		int							      =     residual_kernel_size
						UpperCAmelCase_:		int							      =     dilation_growth_rate
						UpperCAmelCase_:		int							      =     use_causal_conv
						UpperCAmelCase_:		Union[str, Any]							      =     pad_mode
						UpperCAmelCase_:		Tuple							      =     compress
						UpperCAmelCase_:		Optional[int]							      =     num_lstm_layers
						UpperCAmelCase_:		str							      =     trim_right_ratio
						UpperCAmelCase_:		List[str]							      =     codebook_size
						UpperCAmelCase_:		List[str]							      =     codebook_dim if codebook_dim is not None else hidden_size
						UpperCAmelCase_:		Optional[int]							      =     use_conv_shortcut

						if self.norm_type not in ["weight_norm", "time_group_norm"]:
									raise ValueError(
									    f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'					)

						super().__init__(**SCREAMING_SNAKE_CASE_					)

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						if self.chunk_length_s is None:
									return None
						else:
									return int(self.chunk_length_s * self.sampling_rate					)

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						if self.chunk_length_s is None or self.overlap is None:
									return None
						else:
									return max(1,   int((1.0 - self.overlap) * self.chunk_length					)					)

			@property
			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		Any							      =     np.prod(self.upsampling_ratios					)
						return math.ceil(self.sampling_rate / hop_length					)

			@property
			def      __snake_case       (self					)       ->   int:
						return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a      :  Dict			     =			{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Any			     =			['LxmertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'LxmertEncoder',
	    'LxmertForPreTraining',
	    'LxmertForQuestionAnswering',
	    'LxmertModel',
	    'LxmertPreTrainedModel',
	    'LxmertVisualFeatureEncoder',
	    'LxmertXLayer',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLxmertForPreTraining',
	    'TFLxmertMainLayer',
	    'TFLxmertModel',
	    'TFLxmertPreTrainedModel',
	    'TFLxmertVisualFeatureEncoder',
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	a      :  str			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






